1 The method
1.1 Recruitment
Since June 2020, I have interviewed 57 of the top social, behavioral, and political scientists, along with several eminent philosophers, wisdom scientists, and futurists. First, the advisory board and I nominated 153 top scholars from around the world. From the list of contacted nominees, 65% responses to the initial inquiry to participate in the interview series. Among those who responded, 37% declined. I ended up scheduling 38% of the scholars on the initial list – a good response rate considering the pandemic-related challenges and the nature of the project.
1.2 Quantifying narratives
To quantify themes in interviews, we relied on a novel Multi-step Bottom-up/Top-down Cross-validation method for analyzing and cross-referencing unstructured qualitative data. in the method, we first rely on classic grounded approach, with two scholars going through the first set of 30 interviews 9June-July) to identify unique themes for each question in an iterative fashion. Hereby, we followed the following guidelines:
- Each theme should be present at least twice across interviews;
- Themes should have minimum overlap. In other words, they should not be directly reducible, though they could show natural dependencies (e.g., “importance of social connections” and “social support”).
In this initial phase, two independent raters, one of whom was blind to the identity of the interviewees, coded statements on prevalence of determined themes. Initial reliability was good (over 90% agreement), with disagreements resolved via group discussion with the senior scholar. Following iterative procedure, in case additional themes were identified that were not covered by the original categories, they were added to the codebook and statements were recoded for presence of the category.
In the second stage, after interviews were extended into September - December 2020, another two coders (one of whom coded the initial set of 30 interviews) coded the new batch of interviews. Once again, agreement was high (90+%), with disagreements resolved in a group discussion. In this stage, coders identified several additional themes, which were again added to the codebook, with all interviewed cross-examined for presense of these themes. The procedure was repeated four times, until we identified the final list of themes for each question. Here, we also included some simple-occurance themes if they were fully distinct and addressed the questions.
Coding open-ended interviews is inherently subjective. Even in the presence of high reliability between coders (as in our case), validity of the coding may be compromised due to various additional factors (e.g., a particular sentiment in a response, agreement with the opinion raised in the interview response). To address this issue, we introduce a novel top-down cross-validation approach:
- A new, unbiased person blind to identity of interviewees reviews codes and respective transcripts, with the task to identify one key sentence [or key phrases, in case the theme is not captured by a single sentence] from each person’s response to represent their code, guided by the codebook definitions;
- Two further individuals, incl. the senior author of the project, review these key sentences and flag any categories that required adjustment.
The idea behind this cross-validation approach is that this top-down, bird’s eye view allows for greater clarity when matching codes and themes compared to the classical grounded analysis. By matching each code to a core statement/phrase(s), one introduces extra rigor when evaluating each and every code. Indeed, in the process of such cross-validation, several minor inconsistencies were spotted and corrected prior to conducing subsequent analyses.
2 Descriptives
Beyond US, scientists from Australia, Canada, Korea, Japan, HK, Russia, Germany, Spain, Switzerland, Israel, UK.
Past and present presidents/chairs of APS, APA, Psychonomic Society, Society for Personality and Social Psychology, International Association for Cross-cultural Psychology, Society for Affective Science, Cultural Evolution Society, Human Behavior and Evolution Society.
2.1 Breakdown by gender, location, and expertise
SC - socio-cultural expertise
Non-SC - no socio-cultural expertise
Expertise is roughly equally distributed.
Only 20% were familiar with wisdom research (either in philosophy or in psychology).
3 Positive Consequences
3.1 Summary
3.1.1 A great degree of variability in themes
We identified 20 distinct themes.
Most themes mentioned by less than 10% of the interviewees.
Only three themes were mentioned by at least ten people: greater (appreciation of) social connectedness, opportunity for political engagement/structural change viewed, and solidarity.
- Greater (appreciation of) social connectedness, and opportunity for political engagement/structural change viewed, and solidarity as most pos outcomes.
- Additionally: opportunity to re-evaluate habits and embracing new technology.
- Greater (appreciation of) social connectedness and solidarity, as well as resilience more frequent among women, and opportunity for political engagement/structural change and reconsider habits more frequent among men. Reflecting gender roles?
- Socio-cultural experts are more likely to mention social connectedness and opportunity for political engagement/structural change, whereas non-SC experts folked more on reconsidering habits.
3.2 Frequency Chart
3.3 How many themes per person?
Did people just report 1-2 themes or a great number of themes? How do such trends vary across people? 
The majority of people mention only one or two themes here. The median was two themes (as indicated by vertical red line). One-fifth mentioned three themes, and only 3 people mentioned 4 themes (and one person mentioned five themes).
3.4 Frequencies by Gender and Field (Socio-cultural vs. Other fields)
What is important here is that the focus on social-structural issues was not only present among experts in social psychology or cultural matters but also among experts studying other areas of psychology or political science.
3.5 PCA
We can address the question of diversity by examining the degree to which scores across themes are reducible to common components. To this end, we perform a principal component analysis (PCA), which works on dichotomous data.
It appears that 8 component is good start to account for 20 themes for Question 1.
Keep in mind that reducing 20 items to 8 components is just a 40% reduction and that’s not much. Moreover, when we reduce the items to 8 components, the first component explains only 9 % of the variance. Given that each theme by default explains 5% of the variance (1/20), this is not much.
Furthermore, each of these components is largely based on 1-2 item (loadings > .6), with two items for TC4 - solidarity & prosocial behavior, TC6 - shared humanity &bipartisanship, TC3 - critical thinking & increased interest in science, TC5 - personal resilience & optimism/positivity, TC2 - health/wellbeing & worklife balance.
##
## Very Simple Structure
## Call: vss(x = all.data[Q1], rotate = "oblimin", fm = "pc")
## VSS complexity 1 achieves a maximimum of 0.59 with 8 factors
## VSS complexity 2 achieves a maximimum of 0.67 with 8 factors
##
## The Velicer MAP achieves a minimum of 0.02 with 1 factors
## BIC achieves a minimum of NA with factors
## Sample Size adjusted BIC achieves a minimum of NA with factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex eChisq
## 1 0.20 0.00 0.021 0 NA NA 22.0 0.20 NA NA NA NA NA
## 2 0.30 0.34 0.026 0 NA NA 18.1 0.34 NA NA NA NA NA
## 3 0.37 0.44 0.030 0 NA NA 15.0 0.45 NA NA NA NA NA
## 4 0.40 0.49 0.034 0 NA NA 12.4 0.55 NA NA NA NA NA
## 5 0.48 0.57 0.037 0 NA NA 10.1 0.63 NA NA NA NA NA
## 6 0.53 0.61 0.042 0 NA NA 8.3 0.70 NA NA NA NA NA
## 7 0.55 0.66 0.051 0 NA NA 6.7 0.76 NA NA NA NA NA
## 8 0.59 0.67 0.057 0 NA NA 5.4 0.80 NA NA NA NA NA
## SRMR eCRMS eBIC
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## 7 NA NA NA
## 8 NA NA NA## Principal Components Analysis
## Call: principal(r = all.data[Q1], nfactors = 8, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC4 TC1 TC6 TC3 TC5
## shared.humanity_q1 -0.05 -0.12 0.81 -0.01 0.32
## critical.thinking_q1 -0.03 0.15 -0.04 0.86 0.03
## personal.resilience_q1 0.08 0.23 -0.13 0.02 0.69
## gratitude_q1 0.05 0.12 -0.02 -0.12 -0.28
## nature_q1 -0.20 -0.09 0.09 -0.06 0.51
## improved.care.for.elders_q1 -0.06 -0.58 -0.10 0.05 -0.09
## embrace.new.tech_q1 -0.18 0.24 -0.15 -0.28 0.00
## prosocial.behavior_q1 0.87 -0.05 -0.14 -0.10 0.05
## social.connectedness_q1 -0.26 0.53 -0.18 0.07 -0.12
## improved.work.life.balance_q1 -0.10 0.07 0.01 -0.08 -0.16
## increased.interest.in.science_q1 -0.03 -0.09 0.00 0.86 -0.07
## learning.from.pandemics_q1 -0.14 0.22 -0.05 -0.16 -0.09
## political.engagement...structural.change_q1 -0.15 -0.79 -0.02 -0.09 -0.11
## optimism...positivity_q1 -0.06 0.02 0.09 -0.05 0.74
## reconsidering.habits_q1 -0.15 0.25 0.01 0.04 -0.03
## living.in.the.moment_q1 -0.10 0.16 -0.05 -0.01 0.02
## health...well.being_q1 0.01 0.02 -0.10 0.03 0.08
## solidarity_q1 0.84 0.16 0.11 0.05 -0.09
## bipartisanship.and.international.cooperation_q1 0.00 0.12 0.87 -0.03 -0.24
## sympathy...compassion_q1 -0.15 -0.29 -0.28 -0.06 -0.04
## TC2 TC8 TC7 h2 u2 com
## shared.humanity_q1 -0.06 0.13 0.08 0.82 0.18 1.5
## critical.thinking_q1 -0.05 0.08 -0.13 0.83 0.17 1.1
## personal.resilience_q1 -0.03 0.16 -0.22 0.62 0.38 1.7
## gratitude_q1 -0.09 0.39 0.53 0.54 0.46 2.8
## nature_q1 0.26 -0.18 0.21 0.53 0.47 2.8
## improved.care.for.elders_q1 -0.04 0.23 -0.04 0.37 0.63 1.5
## embrace.new.tech_q1 -0.12 0.51 -0.02 0.54 0.46 2.8
## prosocial.behavior_q1 0.00 0.16 -0.02 0.80 0.20 1.2
## social.connectedness_q1 -0.30 0.19 0.14 0.61 0.39 3.2
## improved.work.life.balance_q1 0.81 0.19 -0.15 0.75 0.25 1.3
## increased.interest.in.science_q1 0.01 -0.02 0.10 0.74 0.26 1.1
## learning.from.pandemics_q1 -0.08 -0.75 -0.12 0.61 0.39 1.5
## political.engagement...structural.change_q1 -0.15 0.07 -0.07 0.67 0.33 1.2
## optimism...positivity_q1 -0.13 -0.01 0.06 0.57 0.43 1.1
## reconsidering.habits_q1 0.15 0.45 -0.51 0.64 0.36 2.9
## living.in.the.moment_q1 0.13 0.08 0.78 0.68 0.32 1.2
## health...well.being_q1 0.82 -0.11 0.20 0.77 0.23 1.2
## solidarity_q1 -0.07 -0.11 -0.01 0.78 0.22 1.2
## bipartisanship.and.international.cooperation_q1 -0.02 -0.07 -0.10 0.82 0.18 1.2
## sympathy...compassion_q1 -0.12 -0.20 -0.13 0.23 0.77 4.2
##
## TC4 TC1 TC6 TC3 TC5 TC2 TC8 TC7
## SS loadings 1.73 1.72 1.66 1.64 1.62 1.63 1.52 1.40
## Proportion Var 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.07
## Cumulative Var 0.09 0.17 0.26 0.34 0.42 0.50 0.58 0.65
## Proportion Explained 0.13 0.13 0.13 0.13 0.13 0.13 0.12 0.11
## Cumulative Proportion 0.13 0.27 0.40 0.52 0.65 0.77 0.89 1.00
##
## With component correlations of
## TC4 TC1 TC6 TC3 TC5 TC2 TC8 TC7
## TC4 1.00 -0.02 0.01 0.00 -0.01 -0.07 -0.06 -0.03
## TC1 -0.02 1.00 -0.11 0.06 -0.06 0.02 0.16 -0.06
## TC6 0.01 -0.11 1.00 -0.03 0.07 -0.04 -0.15 -0.03
## TC3 0.00 0.06 -0.03 1.00 0.03 -0.03 0.00 -0.06
## TC5 -0.01 -0.06 0.07 0.03 1.00 0.05 -0.01 0.03
## TC2 -0.07 0.02 -0.04 -0.03 0.05 1.00 -0.01 0.06
## TC8 -0.06 0.16 -0.15 0.00 -0.01 -0.01 1.00 -0.04
## TC7 -0.03 -0.06 -0.03 -0.06 0.03 0.06 -0.04 1.00
##
## Mean item complexity = 1.8
## Test of the hypothesis that 8 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.08
## with the empirical chi square 139.34 with prob < 1.2e-08
##
## Fit based upon off diagonal values = 0.67In short, principal components (note: identical results with minres factor analyses with tetriconic correlation) show substantial diversity, with the top factors shared humanity and critical thinking not accounting for more than 9% of the variance and showing only weak intercorrelation among themselves. And even with 7 components, we don’t account for 90% of the variance.
Three large clusters emerge, similar results are also evident in the dendogram below .
3.6 Network Graph
As we can see above, even after removing negligible correlations (r < .17), cluster analyses on top of the network graphs show five clusters - psych. well-being, civic (poli.structural change, care for elders), solidarity, “pandemic-experience” strategies (social connectedness, change in habits, critical thinking), and adaptive mindsets (shared humanity, focus on nature, optimism/positivity, resilience). Again, this suggests a diversity of topics. Despite forming meaningful dependencies, themes for positive consequences of the pandemic appear largely distinct, except for link between social connectedness and gratitude, linking two networks.
3.7 Convergence vs. divergence of themes over time
I started to interview people in late June, 2020, in the weeks following the BLM protests and riots following George Floyd’s death. Over the course of the summer and the fall, numerous other events occurred, including relaxing and re-introducing lockdown policies and regulations in different parts of the world, start of in-person schooling in the fall, the US Presidential election, and its aftermath.
One way to address the diversity and uncertainty of scientific perspectives on World after Covid and wisdom needed is to examine how consistently themes were mentioned over time. In what follows I do this separately for each question.
First, I bin frequencies of themes by month. Because only two people provided their interviews in early December, 2020, these individuals will be binned with November scores, such that the scores range from 17 (June), and 19 (July), to six (November).
## # A tibble: 6 x 2
## # Groups: month [6]
## month n
## <dbl> <int>
## 1 6 17
## 2 7 19
## 3 9 7
## 4 10 8
## 5 11 4
## 6 12 2## # A tibble: 41 x 2
## # Groups: date [41]
## date n
## <dttm> <int>
## 1 2020-06-17 00:00:00 1
## 2 2020-06-18 00:00:00 4
## 3 2020-06-19 00:00:00 2
## 4 2020-06-20 00:00:00 1
## 5 2020-06-22 00:00:00 1
## 6 2020-06-23 00:00:00 1
## 7 2020-06-25 00:00:00 2
## 8 2020-06-26 00:00:00 3
## 9 2020-06-28 00:00:00 1
## 10 2020-06-29 00:00:00 1
## # ... with 31 more rows##
## 1. June 2. July 3. Sept/early Oct 4. late Oct+
## 17 19 7 14
Focus on social issues declines in frequency being mentioned after June/July, whereas focus on prosociality (social connectedness/solidarity) increases, emerging as dominant in late October right around the time of US election.
Quest for knowledge (i.e., appreciation of science and willingness to learn from the pandemic) was chiefly mentioned early on, but re-emerged again in late November (after BioNtech vaccine was announced to be possibly 90% effective - Nov 9, 2020).
Other themes remain largely identical in frequency of being mentioned over time.
It is also interesting to see how ranking of categories changes over time. Prosocial themes are top at the beginning and esp. in the second wave of the pandemic. Societal issues is close second at the beginning, but move to close to last at the end.
Psych. Wellbeing emerges as the lead in the summer but becomes relatively less pronounced in late Oct/Nov.
Though speculative and post-hoc, this observation suggests that for some of the major themes (incl the most frequently mentioned onces), salience of present-day events inform the themes scientists mentioned in their reflections concerning positive consequences -several years- following the pandemic!
4 Negative Consequences
4.1 Summary
4.1.1 A great degree of variability in themes (again)
We identified 22 distinct themes.
More than half of themes mentioned by less than 10% of the interviewees.
Only two themes were mentioned by at least ten people: political conflict and prejudice and racism.
Generally:
* Political conflict, prejudice/racism, mistrust, social inequality are among the top negative consequences predicted.
* Consequences are either social or societal/ with health/well-being being mentioned relatively less.
* Women more likely to focus on well-being-risks, incl. loneliness and estrangement/alienation, as well as economic hardships. Men more likely to consider prejudice, erosion of democracy.
4.2 Frequency Chart
4.3 How many themes per person?
Did people just report 1-2 themes or a great number of themes? How do such trends vary across people?
It turns out most people mentioned just one or two (median) negative consequences. Two people mentioned up 5 and 7 themes, respectively.
4.4 Frequency Chart by Gender and Field
It is possible that socio-cultural experts focus more on social-structural issues than mental health and other possible domains of change because of idiosyncratic preferences.
As the distribution of themes by field of expertise indicates, this is not likely to be the reason for mentioning political and interpersonal issues most (relative to mental health), with roughly similar distributions. The only exception is that sociocultural experts tended to emphasize mistrust, whereas non-SC experts emphasized irrationality/mispercetion of the world more.
4.5 PCA
##
## Very Simple Structure
## Call: vss(x = all.data[Q3], rotate = "oblimin", fm = "pc")
## VSS complexity 1 achieves a maximimum of 0.58 with 8 factors
## VSS complexity 2 achieves a maximimum of 0.68 with 8 factors
##
## The Velicer MAP achieves a minimum of 0.02 with 1 factors
## BIC achieves a minimum of NA with factors
## Sample Size adjusted BIC achieves a minimum of NA with factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex eChisq
## 1 0.22 0.00 0.023 0 NA NA 25.2 0.22 NA NA NA NA NA
## 2 0.31 0.36 0.025 0 NA NA 20.5 0.36 NA NA NA NA NA
## 3 0.39 0.47 0.026 0 NA NA 16.4 0.49 NA NA NA NA NA
## 4 0.44 0.54 0.027 0 NA NA 13.2 0.59 NA NA NA NA NA
## 5 0.46 0.58 0.029 0 NA NA 10.8 0.67 NA NA NA NA NA
## 6 0.50 0.62 0.033 0 NA NA 8.7 0.73 NA NA NA NA NA
## 7 0.53 0.65 0.038 0 NA NA 7.2 0.78 NA NA NA NA NA
## 8 0.58 0.68 0.044 0 NA NA 5.9 0.82 NA NA NA NA NA
## SRMR eCRMS eBIC
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## 7 NA NA NA
## 8 NA NA NA## Principal Components Analysis
## Call: principal(r = all.data[Q3], nfactors = 8, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC2 TC3 TC6 TC4 TC5 TC7
## estrangement...alienation_q3 -0.20 -0.21 -0.77 -0.11 0.14 -0.15 -0.06
## career.disruptions_q3 -0.10 0.72 0.14 -0.18 -0.16 -0.08 0.06
## decline.in.quality.of.education_q3 0.84 -0.06 0.00 -0.02 0.04 -0.06 -0.02
## decreased.trust.in.science_q3 0.00 -0.19 0.12 0.28 0.18 0.31 0.54
## decreased.well.being_q3 -0.10 -0.08 -0.27 0.08 -0.04 -0.12 0.06
## economic.hardship_q3 -0.03 0.89 0.01 -0.01 0.12 0.00 -0.07
## educational.inequality_q3 0.35 -0.12 0.10 -0.05 -0.75 -0.09 0.02
## erosion.of.democratic.institutions_q3 -0.07 0.04 -0.03 -0.05 0.02 -0.07 0.86
## exacerbated.social.inequality_q3 -0.21 0.01 0.07 -0.03 -0.79 0.05 -0.12
## mistrust_q3 0.06 -0.31 0.22 -0.30 0.23 -0.48 0.15
## prejudice...racism_q3 -0.18 -0.21 0.57 -0.11 0.29 -0.08 -0.30
## despair_q3 -0.02 0.54 0.04 0.29 0.12 -0.12 0.24
## prioritize.self.over.others_q3 -0.05 -0.16 -0.08 0.48 -0.02 0.28 -0.22
## irrationality_q3 -0.02 -0.10 0.16 -0.27 0.12 0.52 0.38
## loneliness_q3 0.26 0.03 -0.69 -0.07 0.22 0.06 -0.12
## misperception.about.the.world_q3 -0.01 -0.13 0.22 0.13 0.11 0.66 -0.04
## political.conflict_q3 -0.10 -0.13 0.15 0.72 0.14 -0.07 -0.04
## authoritarianism_q3 0.21 0.08 0.28 0.37 0.13 -0.51 0.09
## stunted.child.development_q3 0.88 -0.10 0.01 -0.01 -0.13 -0.06 0.00
## pessimism_q3 0.00 0.13 -0.01 0.60 -0.10 0.06 0.18
## intimate.relations_q3 0.61 0.26 -0.12 -0.08 0.23 0.22 -0.14
## autobiographical.memory_q3 0.02 -0.01 0.21 -0.13 0.08 0.11 -0.16
## TC8 h2 u2 com
## estrangement...alienation_q3 0.05 0.74 0.26 1.5
## career.disruptions_q3 -0.13 0.62 0.38 1.5
## decline.in.quality.of.education_q3 -0.02 0.72 0.28 1.0
## decreased.trust.in.science_q3 0.00 0.60 0.40 3.0
## decreased.well.being_q3 0.75 0.72 0.28 1.4
## economic.hardship_q3 -0.02 0.80 0.20 1.1
## educational.inequality_q3 0.04 0.75 0.25 1.6
## erosion.of.democratic.institutions_q3 -0.09 0.75 0.25 1.1
## exacerbated.social.inequality_q3 -0.09 0.69 0.31 1.2
## mistrust_q3 -0.07 0.55 0.45 4.0
## prejudice...racism_q3 -0.12 0.62 0.38 3.0
## despair_q3 0.17 0.54 0.46 2.5
## prioritize.self.over.others_q3 -0.21 0.43 0.57 3.0
## irrationality_q3 -0.04 0.50 0.50 2.9
## loneliness_q3 -0.10 0.65 0.35 1.7
## misperception.about.the.world_q3 0.01 0.57 0.43 1.5
## political.conflict_q3 -0.15 0.63 0.37 1.4
## authoritarianism_q3 0.01 0.55 0.45 3.2
## stunted.child.development_q3 -0.01 0.82 0.18 1.1
## pessimism_q3 0.19 0.49 0.51 1.6
## intimate.relations_q3 -0.05 0.57 0.43 2.3
## autobiographical.memory_q3 0.82 0.70 0.30 1.3
##
## TC1 TC2 TC3 TC6 TC4 TC5 TC7 TC8
## SS loadings 2.25 2.04 1.83 1.71 1.66 1.53 1.54 1.44
## Proportion Var 0.10 0.09 0.08 0.08 0.08 0.07 0.07 0.07
## Cumulative Var 0.10 0.20 0.28 0.36 0.43 0.50 0.57 0.64
## Proportion Explained 0.16 0.15 0.13 0.12 0.12 0.11 0.11 0.10
## Cumulative Proportion 0.16 0.31 0.44 0.56 0.68 0.79 0.90 1.00
##
## With component correlations of
## TC1 TC2 TC3 TC6 TC4 TC5 TC7 TC8
## TC1 1.00 0.01 -0.03 -0.05 -0.05 -0.06 -0.04 -0.03
## TC2 0.01 1.00 -0.06 0.04 -0.06 -0.01 0.04 0.02
## TC3 -0.03 -0.06 1.00 0.07 -0.01 0.01 0.08 -0.11
## TC6 -0.05 0.04 0.07 1.00 0.05 0.05 0.12 0.02
## TC4 -0.05 -0.06 -0.01 0.05 1.00 0.06 0.02 -0.03
## TC5 -0.06 -0.01 0.01 0.05 0.06 1.00 -0.04 -0.04
## TC7 -0.04 0.04 0.08 0.12 0.02 -0.04 1.00 0.03
## TC8 -0.03 0.02 -0.11 0.02 -0.03 -0.04 0.03 1.00
##
## Mean item complexity = 1.9
## Test of the hypothesis that 8 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.08
## with the empirical chi square 155.71 with prob < 2.4e-06
##
## Fit based upon off diagonal values = 0.73Similar to positive consequences, we can address the question of diversity by examining the degree to which scores across themes are reducible to common component, examining principal component analysis (PCA).
It appears that 7 or 8 components is as good a start to account for 22 themes for Question 3 (neg consequences), as any other. This said, the scree plot line is pretty flat.
Again, keep in mind that reducing 22 items to 8 components is not much. Moreover, when we reduce the items to 8 components, the first component explains only 10 % of the variance and in total 8 components account only for 64%.
Given that each theme by default explains 4.6% of the variance (1/22), this is not much.
Furthermore, each of these components is largely based on 1-2 items (loadings > .6), with an exception of TC1 - decline in education, stunted child dev & intomate relationships
In short, principal components show substantial diversity, with the top factors political conflict and prejudice/racism not accounting for more than 16% of the variance and showing only weak intercorrelation among themselves
4.6 Network Graph
As we can see above, even after removing negligible correlations (r < .17), cluster analyses on top of the network graphs show four clusters - Four clusters: irrationality & misperception of the world, prejudice, & decline of democracy / well-being decline and associated factors / family matters & educational (social) inequality (incl stunted child dev) / economic hardships.
Themes for negative consequences of the pandemic appear largely distinct, except for link between loneliness and gratitude, linking two networks concerning family matters (loneliness) and wellbeing (alienation/estrangement).
4.7 Convergence vs. divergence of themes over time
As a reminder: I started to interview people in late June, 2020, in the weeks following the BLM protests and riots following George Floyd’s death. Over the course of the summer and the fall, numerous other events occurred, including relaxing and re-introducing lockdown policies and regulations in different parts of the world, start of in-person schooling in the fall, the US Presidential election, and its aftermath.
Once again, we binned scores in the the same four groups as above: June, July, Sept/early Oct, and second part of Oct-early Dec.
Focus on societal issues consistently remains the most frequent topic over time.
Whereas discussion of health and well-being was the second most frequent category mentioned for negative consequences in June, it become third/forth frequent from July on.
In exchange, irrationality started to increase in frequency being mentioned in the fall, closer to the US Election and second lockdowns.
Least frequently mentioned were concerns about work.
Like for positive consequences, one can post-hoc speculate that some of the major themes such as well-being related concerns (e.g., loneliness, well-being decline), and forecasts of irrationality (incl. mispeception of the world, mistrust in science) were likely impacted by societal events unfolding around the time people were making forecasts.
Notably, one theme remained pretty stable over time, with negative societal consequences (political conflict, prejudice and racism, societal inequality) - the most frequently mentioned topic in response to this question - remained consistently most frequent over time.
5 Comparison of Positive and Negative Consequences Side by Side
6 Wisdom for Positive Consequences
6.1 Summary
We identified 21 distinct categories. Most themes mentioned by less than 10% of the interviewees.
Only one theme was mentioned by at least ten people: Need for solidarity.
Most common wisdom-related strategies to sustain positive changes (7+ ppl) concern solidarity, willingness to engage in political and structure change, perspective-taking, and critical thinking.
6.2 Frequency Chart
6.3 How many themes per person?
Did people just report 1-2 themes or a great number of themes? How do such trends vary across people?
It turns out most people mentioned just one or two (median) negative consequences. Two people mentioned up to 4 and 5 themes, respectively.
6.4 Frequencies by Gender and Field
It is not surprising that wisdom experts would mention meta-cognitive and moral considerations. The question is whether scientists without much familiarity with the science of wisdom would mention similar constructs. It appears that is the case, with non-wisdom experts emphasizing such meta-cognitive categories as critical thinking, intellectual humility, acknowledgment of uncertainty, sympathy and compassion and perspective-taking, along with moral aspirations about solidarity, social support, and paying greater attention to one’s family and relationships, and promoting societal change toward fair and just society.
6.5 PCA
##
## Very Simple Structure
## Call: vss(x = all.data[Q2], rotate = "oblimin", fm = "pc")
## VSS complexity 1 achieves a maximimum of 0.56 with 8 factors
## VSS complexity 2 achieves a maximimum of 0.64 with 8 factors
##
## The Velicer MAP achieves a minimum of 0.01 with 1 factors
## BIC achieves a minimum of NA with factors
## Sample Size adjusted BIC achieves a minimum of NA with factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex eChisq
## 1 0.20 0.00 0.015 0 NA NA 22.0 0.20 NA NA NA NA NA
## 2 0.32 0.34 0.018 0 NA NA 18.3 0.34 NA NA NA NA NA
## 3 0.38 0.43 0.021 0 NA NA 15.4 0.44 NA NA NA NA NA
## 4 0.44 0.49 0.024 0 NA NA 13.1 0.52 NA NA NA NA NA
## 5 0.47 0.55 0.028 0 NA NA 10.9 0.60 NA NA NA NA NA
## 6 0.49 0.57 0.033 0 NA NA 9.1 0.67 NA NA NA NA NA
## 7 0.54 0.63 0.038 0 NA NA 7.5 0.73 NA NA NA NA NA
## 8 0.56 0.64 0.044 0 NA NA 6.2 0.78 NA NA NA NA NA
## SRMR eCRMS eBIC
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## 7 NA NA NA
## 8 NA NA NA## Principal Components Analysis
## Call: principal(r = all.data[Q2], nfactors = 8, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC2 TC3 TC7 TC4
## social.connectedness_q2 0.65 0.02 0.26 -0.08 0.00
## social.support_q2 0.64 -0.08 -0.10 -0.02 0.15
## evidence.based.decision.making_q2 -0.08 -0.03 0.01 -0.11 -0.09
## improved.work.life.balance_q2 0.74 -0.09 -0.09 -0.04 0.04
## acknowledge.of.uncertainty.flexibility_q2 -0.12 0.62 -0.10 -0.12 0.10
## balance.of.personal...others.interest_q2 0.79 0.05 -0.07 -0.04 -0.02
## perspective.taking_q2 0.17 0.62 0.02 0.02 -0.19
## critical.thinking_q2 -0.13 -0.14 -0.05 0.74 -0.01
## bipartisanship.and.international.cooperation_q2 -0.16 -0.22 -0.24 -0.21 0.39
## solidarity_q2 -0.11 0.38 -0.20 0.04 0.42
## improve.communication_q2 0.00 -0.05 0.79 0.15 0.13
## intellectual.humility_q2 -0.03 0.00 0.01 0.76 0.04
## living.in.the.moment_q2 -0.08 -0.02 -0.11 -0.22 -0.70
## learning.from.pandemics_q2 -0.06 -0.01 -0.10 0.02 0.13
## political.engagement...structural.change_q2 -0.10 -0.40 -0.20 -0.34 0.12
## self.reflection.on.what.s.important_q2 -0.07 0.00 -0.16 0.15 -0.73
## shared.humanity_q2 -0.21 -0.08 0.04 -0.14 -0.01
## sympathy...compassion_q2 0.27 0.01 -0.15 0.04 0.12
## self.distancing_q2 -0.05 -0.10 -0.07 -0.10 0.01
## embrace.new.tech_q2 -0.10 -0.01 0.76 -0.22 0.05
## personal.resilience_q2 -0.08 0.80 -0.04 -0.08 0.06
## TC5 TC6 TC8 h2 u2
## social.connectedness_q2 0.02 -0.07 -0.07 0.497 0.50
## social.support_q2 0.04 0.03 0.25 0.518 0.48
## evidence.based.decision.making_q2 0.07 0.12 -0.18 0.071 0.93
## improved.work.life.balance_q2 0.00 0.11 -0.11 0.595 0.41
## acknowledge.of.uncertainty.flexibility_q2 -0.44 0.21 -0.20 0.734 0.27
## balance.of.personal...others.interest_q2 0.01 -0.02 -0.04 0.626 0.37
## perspective.taking_q2 0.08 -0.34 0.17 0.606 0.39
## critical.thinking_q2 0.11 0.21 -0.24 0.697 0.30
## bipartisanship.and.international.cooperation_q2 0.37 -0.30 0.01 0.614 0.39
## solidarity_q2 0.48 -0.15 0.14 0.700 0.30
## improve.communication_q2 0.05 -0.08 -0.06 0.683 0.32
## intellectual.humility_q2 -0.12 -0.25 0.06 0.645 0.35
## living.in.the.moment_q2 0.15 -0.01 -0.14 0.553 0.45
## learning.from.pandemics_q2 -0.87 -0.14 0.11 0.793 0.21
## political.engagement...structural.change_q2 -0.13 -0.28 -0.37 0.602 0.40
## self.reflection.on.what.s.important_q2 0.09 -0.16 0.16 0.642 0.36
## shared.humanity_q2 0.06 0.60 0.36 0.529 0.47
## sympathy...compassion_q2 0.11 0.70 0.00 0.636 0.36
## self.distancing_q2 -0.10 0.06 0.85 0.745 0.25
## embrace.new.tech_q2 0.07 -0.01 0.01 0.594 0.41
## personal.resilience_q2 0.08 0.00 -0.13 0.674 0.33
## com
## social.connectedness_q2 1.4
## social.support_q2 1.5
## evidence.based.decision.making_q2 4.1
## improved.work.life.balance_q2 1.2
## acknowledge.of.uncertainty.flexibility_q2 2.7
## balance.of.personal...others.interest_q2 1.0
## perspective.taking_q2 2.2
## critical.thinking_q2 1.6
## bipartisanship.and.international.cooperation_q2 5.3
## solidarity_q2 3.9
## improve.communication_q2 1.2
## intellectual.humility_q2 1.3
## living.in.the.moment_q2 1.5
## learning.from.pandemics_q2 1.2
## political.engagement...structural.change_q2 5.0
## self.reflection.on.what.s.important_q2 1.4
## shared.humanity_q2 2.1
## sympathy...compassion_q2 1.5
## self.distancing_q2 1.1
## embrace.new.tech_q2 1.2
## personal.resilience_q2 1.1
##
## TC1 TC2 TC3 TC7 TC4 TC5 TC6 TC8
## SS loadings 2.26 1.81 1.51 1.51 1.49 1.43 1.41 1.32
## Proportion Var 0.11 0.09 0.07 0.07 0.07 0.07 0.07 0.06
## Cumulative Var 0.11 0.19 0.27 0.34 0.41 0.48 0.54 0.61
## Proportion Explained 0.18 0.14 0.12 0.12 0.12 0.11 0.11 0.10
## Cumulative Proportion 0.18 0.32 0.44 0.56 0.67 0.79 0.90 1.00
##
## With component correlations of
## TC1 TC2 TC3 TC7 TC4 TC5 TC6 TC8
## TC1 1.00 0.01 0.03 0.01 -0.02 0.04 0.09 0.02
## TC2 0.01 1.00 -0.04 0.00 0.03 -0.01 -0.01 0.02
## TC3 0.03 -0.04 1.00 0.10 -0.08 -0.02 0.03 -0.05
## TC7 0.01 0.00 0.10 1.00 -0.10 0.02 0.01 -0.01
## TC4 -0.02 0.03 -0.08 -0.10 1.00 0.02 -0.02 -0.02
## TC5 0.04 -0.01 -0.02 0.02 0.02 1.00 -0.06 0.07
## TC6 0.09 -0.01 0.03 0.01 -0.02 -0.06 1.00 -0.01
## TC8 0.02 0.02 -0.05 -0.01 -0.02 0.07 -0.01 1.00
##
## Mean item complexity = 2.1
## Test of the hypothesis that 8 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.08
## with the empirical chi square 142.28 with prob < 7.6e-07
##
## Fit based upon off diagonal values = 0.62Similar to prior results, we can address the question of diversity by examining the degree to which scores across themes are reducible to common component, examining principal component analysis (PCA).
It appears that 7 or 8 components is as good a start to account for 21 themes for Question 2 (wisdom for pos consequences), as any other. Once again, the scree plot line is pretty flat.
Again, keep in mind that reducing 21 items to 8 components is not parsimonious. Moreover, when we reduce the items to 8 components, the first component explains only 11 % of the variance, and in total 8 components explain 61% of variance.
Given that each theme by default explains 4.8% of the variance (1/21), this is not much.
Furthermore, each of these components is largely based on 1-2 items (loadings > .6), with an exception of TC1 - social connectedness, social support, and balance of personal and others interests, and improved work-life balance.
In short, principal components show substantial diversity, with the top factor solidarity not even loading squarely on any of the components.
6.6 Network Graph
Even after removing negligible correlations (r < .17), cluster analyses on top of the network graphs show seven clusters, two of which are strongly inter-related (social connectedness and improved communication):
• Critical thinking/intellectual humility
• Willingness to adopt/learn new tech
• Meta-cognition: perspective-taking, balancing different interests, acknowledge uncertainty, and resilience
• Social cognitions: Balance of different interests, social connectedness, sympathy, social support
• Big picture focus: Shared humanity and self-distancing
• Solidarity, political engagement & cooperation
• Focus on what’s important/mindfulness of the present
• Key is social connectedness - it connects two clusters. Solidarity also appears critical and close to the center of the network (proximity to most themes)
6.7 Convergence vs. divergence of themes over time
Once again, we binned scores in the the same four groups as above: June, July, Sept/early Oct, and second part of Oct-early Dec.
Focus on meta-cognition consistently remains the most frequent recommendation over time (sharing it with prosocial category in September).
Whereas discussion of societal strategies for wisdom decline in prevalence over time, one strategy clearly picks up in the fall - prosocial strategies!
One can post-hoc speculate about it. It may be due to greater fatigue and U.S. election, with topics like solidarity becoming more important. It may also have something to do with the fact that prosocial themes equally picked up in answer to the question about type of positive change.
In other words, scientists forecast greater prosociality OR they recommend greater prosociality. Both communicate some hope for what may be missing in a current society they live in.
7 Wisdom against Negative Consequences
7.1 Summary
We identified 23 distinct categories. Except for five themes, categories were mentioned by less than 10% of the interviewees.
Only two theme was mentioned by at least ten people: Long-term orientation (16%) and Willingness to introduce political-structural change (24%).
Overall, necessity for political/structure change, long-term focus, and sympathy/compassion are key strategies for mitigating negative consequences of the pandemic, followed by two other social strategies: solidarity, and social support.
Importantly, ten out of 23 identified themes were meta-cognitive in nature, consistent with recent research on meta-cognition being at the heart of wisdom. It appears leading scholars share this intuition for the context of post-pandemic challenges.
7.2 Frequency Chart
7.3 How many themes per person?
Did people just report 1-2 themes or a great number of themes? How do such trends vary across people?
It turns out most people mentioned just one or two (median) negative consequences. Two people mentioned up to 5 and 6 themes, respectively.
7.4 Frequencies by Gender and Field
Once again, it is not surprising that wisdom experts mention meta-cognitive and moral considerations. The question is whether scientists without much familiarity with the science of wisdom would mention similar constructs for this question, too. It appears that is the case, with non-wisdom experts emphasizing such meta-cognitive categories as long-term orientation, sympathy and compassion, context sensitivity, appreciation of the idea of shared humanity, critical thinking and acknowledging uncertainty, along with moral aspirations about solidarity, social support, and paying greater attention to one’s family and relationships, and promoting political cooperation and societal change toward fair and just society. In fact, the latter categories were strongly prevalent among non-wisdom experts.
7.5 PCA
##
## Very Simple Structure
## Call: vss(x = all.data[Q4], rotate = "oblimin", fm = "pc")
## VSS complexity 1 achieves a maximimum of 0.53 with 8 factors
## VSS complexity 2 achieves a maximimum of 0.64 with 8 factors
##
## The Velicer MAP achieves a minimum of 0.02 with 2 factors
## BIC achieves a minimum of NA with factors
## Sample Size adjusted BIC achieves a minimum of NA with factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex eChisq
## 1 0.22 0.00 0.020 0 NA NA 25.2 0.22 NA NA NA NA NA
## 2 0.33 0.35 0.019 0 NA NA 21.1 0.35 NA NA NA NA NA
## 3 0.39 0.43 0.021 0 NA NA 17.7 0.45 NA NA NA NA NA
## 4 0.45 0.51 0.024 0 NA NA 14.8 0.54 NA NA NA NA NA
## 5 0.46 0.57 0.026 0 NA NA 12.4 0.62 NA NA NA NA NA
## 6 0.49 0.60 0.029 0 NA NA 10.2 0.68 NA NA NA NA NA
## 7 0.52 0.62 0.033 0 NA NA 8.3 0.74 NA NA NA NA NA
## 8 0.53 0.64 0.038 0 NA NA 6.9 0.78 NA NA NA NA NA
## SRMR eCRMS eBIC
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## 7 NA NA NA
## 8 NA NA NA## Principal Components Analysis
## Call: principal(r = all.data[Q4], nfactors = 8, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC1 TC2 TC5 TC3 TC4
## social.awareness_q4 -0.07 -0.12 -0.05 0.00 -0.02
## balance.of.personal...others.interest_q4 -0.07 -0.18 0.21 0.66 -0.16
## solidarity_q4 -0.03 0.05 -0.15 0.66 0.24
## context.sensitivity_q4 -0.15 -0.14 -0.22 -0.22 -0.37
## bipartisanship.and.international.cooperation_q4 0.00 0.64 0.06 -0.02 -0.10
## critical.thinking_q4 -0.12 -0.11 -0.27 -0.20 -0.22
## evidence.based.decision.making_q4 -0.09 0.66 -0.23 -0.05 0.00
## gratitude_q4 0.83 0.02 0.05 0.05 0.05
## optimism...positivity_q4 -0.08 -0.24 0.14 -0.33 0.34
## improve.communication_q4 -0.07 -0.10 0.30 -0.01 0.09
## long.term.orientation_q4 -0.04 0.52 0.53 0.16 -0.09
## social.support_q4 -0.06 -0.18 -0.11 -0.05 0.81
## social.connectedness_q4 -0.05 -0.07 -0.21 -0.17 -0.11
## acknowledge.uncertainty.flexibility_q4 0.76 -0.06 -0.10 -0.08 -0.11
## living.in.the.moment_q4 0.91 0.00 0.00 0.00 -0.01
## patience_q4 0.00 0.75 0.01 -0.10 0.01
## perspective.taking_q4 0.04 -0.06 0.15 0.35 0.01
## self.reflection.on.what.s.important_q4 0.23 -0.08 -0.23 -0.10 -0.20
## shared.humanity_q4 -0.03 -0.04 -0.14 0.36 -0.17
## strive.for.socio.economic.equality_q4 -0.06 -0.21 0.72 0.05 -0.16
## sympathy...compassion_q4 -0.04 0.24 -0.05 0.11 0.64
## political.engagement...structural.change_q4 -0.09 0.24 0.57 -0.36 0.03
## self.distancing_q4 -0.18 0.09 -0.23 0.35 -0.16
## TC6 TC7 TC8 h2 u2 com
## social.awareness_q4 0.08 0.80 0.11 0.66 0.34 1.1
## balance.of.personal...others.interest_q4 -0.02 -0.14 0.08 0.59 0.41 1.7
## solidarity_q4 -0.14 0.13 0.04 0.55 0.45 1.6
## context.sensitivity_q4 -0.18 0.49 -0.09 0.52 0.48 3.8
## bipartisanship.and.international.cooperation_q4 -0.32 -0.01 0.17 0.56 0.44 1.7
## critical.thinking_q4 0.31 -0.26 0.24 0.42 0.58 6.2
## evidence.based.decision.making_q4 0.10 -0.20 -0.01 0.51 0.49 1.5
## gratitude_q4 -0.14 0.01 0.02 0.68 0.32 1.1
## optimism...positivity_q4 -0.03 -0.15 0.43 0.50 0.50 4.1
## improve.communication_q4 0.02 -0.20 -0.66 0.58 0.42 1.7
## long.term.orientation_q4 0.14 0.00 0.03 0.68 0.32 2.4
## social.support_q4 -0.06 -0.17 0.12 0.74 0.26 1.3
## social.connectedness_q4 -0.03 -0.03 -0.65 0.53 0.47 1.5
## acknowledge.uncertainty.flexibility_q4 0.20 -0.04 -0.01 0.69 0.31 1.3
## living.in.the.moment_q4 -0.01 -0.03 0.03 0.84 0.16 1.0
## patience_q4 0.03 -0.05 -0.02 0.57 0.43 1.1
## perspective.taking_q4 0.53 0.38 0.14 0.62 0.38 3.0
## self.reflection.on.what.s.important_q4 0.47 -0.15 0.02 0.45 0.55 2.9
## shared.humanity_q4 -0.65 -0.11 0.15 0.62 0.38 2.0
## strive.for.socio.economic.equality_q4 0.03 -0.16 -0.13 0.62 0.38 1.5
## sympathy...compassion_q4 0.16 0.32 -0.22 0.73 0.27 2.3
## political.engagement...structural.change_q4 -0.08 0.11 0.20 0.60 0.40 2.6
## self.distancing_q4 0.45 -0.28 0.15 0.58 0.42 4.4
##
## TC1 TC2 TC5 TC3 TC4 TC6 TC7 TC8
## SS loadings 2.29 2.06 1.73 1.70 1.64 1.53 1.51 1.35
## Proportion Var 0.10 0.09 0.08 0.07 0.07 0.07 0.07 0.06
## Cumulative Var 0.10 0.19 0.26 0.34 0.41 0.48 0.54 0.60
## Proportion Explained 0.17 0.15 0.13 0.12 0.12 0.11 0.11 0.10
## Cumulative Proportion 0.17 0.32 0.44 0.56 0.68 0.79 0.90 1.00
##
## With component correlations of
## TC1 TC2 TC5 TC3 TC4 TC6 TC7 TC8
## TC1 1.00 -0.05 -0.06 -0.04 -0.05 0.04 -0.03 -0.01
## TC2 -0.05 1.00 0.10 0.03 0.04 0.02 0.06 0.02
## TC5 -0.06 0.10 1.00 0.03 0.02 -0.03 -0.01 -0.02
## TC3 -0.04 0.03 0.03 1.00 -0.04 0.07 0.02 0.06
## TC4 -0.05 0.04 0.02 -0.04 1.00 -0.02 0.10 0.01
## TC6 0.04 0.02 -0.03 0.07 -0.02 1.00 0.00 -0.02
## TC7 -0.03 0.06 -0.01 0.02 0.10 0.00 1.00 -0.06
## TC8 -0.01 0.02 -0.02 0.06 0.01 -0.02 -0.06 1.00
##
## Mean item complexity = 2.3
## Test of the hypothesis that 8 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.08
## with the empirical chi square 174.58 with prob < 2.3e-06
##
## Fit based upon off diagonal values = 0.67Similar to prior results, we can address the question of diversity by examining the degree to which scores across themes are reducible to common component, examining principal component analysis (PCA).
It appears that 8 components is as good a start to account for 23 themes for Question 4 (wisdom against neg consequences), as any other. Once again, the scree plot line is pretty flat.
Again, keep in mind that reducing 23 items to 8 components is not parsimonious. Moreover, when we reduce the items to 8 components, the first component explains only 10 % of the variance, and in total 8 components explain 60% of variance.
Given that each theme by default explains 4.4% of the variance (1/23), this is not much.
Furthermore, each of these components is largely based on 1-2 items (loadings > .6).
In short, principal components show substantial diversity, with the top factor political engagement and structural change not even loading squarely on any of the components (and negatively loading on the component it has highest loading for - the one with positive loading for self-distancing).
7.6 Network Graph
Even after removing negligible correlations (r < .17), cluster analyses on top of the network graphs show four clusters, three of which are strongly inter-related (perspective-taking, balance of diverse interests, and socio-econ equality):
• Four clusters:
• Context-sensitivity & perspective-taking
• Meta-cognition : balance, self-distancing, focus on the big picture shared humanity
• Political engagement and structural change, cooperation, as well as fight against inequality, linked with patience /long-term focus
• Psych well-being enhancement: Gratitude, acknowledge uncertainty and focus on the present
Balance of diverse interests appears in the center of everything, connecting to other themes mentioned by experts.
7.7 Convergence vs. divergence of themes over time
Once again, focus on meta-cognition consistently remains the most frequent recommendation over time and in fact, increases in importance over time.
As with wisdom to sustain positive change (Q2), discussion of societal strategies for wisdom against negative changes is less prevalent over time, whereas prosocial tendencies becoming relatively more prevant over time!
One can post-hoc speculate about it. It may be due to greater fatigue and U.S. election, with topics like solidarity becoming more important and focus becoming increasingly within-country centric and less about international cooperation (which would be captured by societal theme). This said both societal and prosocial themes tap into the same general foundation of wisdom - moral aspirations (Grossmannn et al., 2020) - it captures both prosocial concerns on the interpersonal level and broader societal concerns with bipartisanship and the common good.
8 Wisdom Now
8.1 Summary
We identified 24 distinct categories.
Except for four themes, categories were mentioned by less than 10% of the interviewees.
Only two theme was mentioned by at least ten people: Agency & Control (18%) and Social connectedness (25%).
Once again, seven categories were meta-cognitive, whereas 3 were (pro)social and one was socielal.
8.2 Frequency Chart
8.3 How many themes per person?
Did people just report 1-2 themes or a great number of themes? How do such trends vary across people?
Most people reported 1-2 themes, with the max being 4 themes mentioned by 4 people.
The majority of people mention only one or two themes here. The median was two themes (as indicated by vertical red line). One-fifth mentioned three themes, and only 3 people mentioned 4 themes (and one person mentioned five themes).
8.4 Frequencies by Gender and Field
Once again, it is not surprising that wisdom experts mention meta-cognitive and moral considerations. The question is whether scientists without much familiarity with the science of wisdom would mention similar constructs for this question, too. It appears that is the case, with non-wisdom experts emphasizing meta-cognitive categories such as sympathy and compassion, long-term orientation, context sensitivity, critical thinking and acknowledging uncertainty, along with moral aspirations about solidarity, and paying greater attention to one’s family and relationship. In fact, the latter categories were strongly prevalent among non-wisdom experts.
8.5 PCA
##
## Very Simple Structure
## Call: vss(x = all.data[Q5], rotate = "oblimin", fm = "pc")
## VSS complexity 1 achieves a maximimum of 0.51 with 8 factors
## VSS complexity 2 achieves a maximimum of 0.61 with 8 factors
##
## The Velicer MAP achieves a minimum of 0.02 with 1 factors
## BIC achieves a minimum of NA with factors
## Sample Size adjusted BIC achieves a minimum of NA with factors
##
## Statistics by number of factors
## vss1 vss2 map dof chisq prob sqresid fit RMSEA BIC SABIC complex eChisq
## 1 0.16 0.00 0.017 0 NA NA 27.0 0.16 NA NA NA NA NA
## 2 0.25 0.29 0.018 0 NA NA 22.9 0.29 NA NA NA NA NA
## 3 0.32 0.38 0.021 0 NA NA 19.5 0.40 NA NA NA NA NA
## 4 0.35 0.44 0.024 0 NA NA 16.6 0.49 NA NA NA NA NA
## 5 0.39 0.48 0.026 0 NA NA 14.0 0.57 NA NA NA NA NA
## 6 0.45 0.52 0.029 0 NA NA 11.7 0.64 NA NA NA NA NA
## 7 0.49 0.57 0.032 0 NA NA 9.9 0.70 NA NA NA NA NA
## 8 0.51 0.61 0.035 0 NA NA 8.4 0.74 NA NA NA NA NA
## SRMR eCRMS eBIC
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 NA NA NA
## 6 NA NA NA
## 7 NA NA NA
## 8 NA NA NA## Principal Components Analysis
## Call: principal(r = all.data[Q5], nfactors = 8, rotate = "oblimin")
## Standardized loadings (pattern matrix) based upon correlation matrix
## TC2 TC1 TC8 TC5 TC4
## balance.of.personal...others.interest_q5 -0.12 -0.12 -0.09 0.43 0.42
## clear.governmental.communication_q5 -0.04 -0.05 0.01 0.02 0.00
## solidarity_q5 0.05 -0.01 -0.03 0.02 -0.06
## bipartisanship.and.international.cooperation_q5 0.69 -0.23 0.03 0.08 0.01
## critical.thinking_q5 -0.20 0.00 -0.17 0.08 0.72
## embrace.new.tech_q5 -0.03 -0.09 0.27 0.05 0.07
## agency...control_q5 0.07 -0.01 0.04 -0.72 -0.07
## follow.rules_q5 -0.15 0.18 0.65 -0.07 -0.13
## optimism...positivity_q5 -0.07 -0.09 0.84 0.08 0.02
## learning.from.the.pandemics_q5 0.76 0.02 0.04 -0.03 -0.04
## improve.communication_q5 -0.11 -0.06 -0.14 0.09 -0.33
## prosocial.behavior_q5 -0.20 0.71 -0.15 0.19 -0.05
## long.term.orientation_q5 0.78 0.15 -0.23 0.05 -0.02
## acknowledge.uncertainty.flexibility_q5 -0.17 -0.11 -0.22 -0.66 0.18
## living.in.the.moment_q5 0.02 -0.03 -0.08 0.06 -0.10
## patience_q5 0.10 0.65 0.36 0.08 0.02
## perspective.taking_q5 -0.19 -0.04 -0.12 -0.39 0.38
## self.reflection.on.what.is.important_q5 -0.18 -0.29 -0.13 0.17 -0.27
## social.connectedness_q5 -0.28 -0.05 -0.33 0.20 -0.61
## self.distancing_q5 0.14 -0.10 -0.12 0.45 0.38
## strive.for.socio.economic.equality_q5 -0.02 -0.02 0.11 0.06 0.03
## sympathy...compassion_q5 -0.10 -0.07 -0.12 -0.01 -0.11
## take.science.seriously_q5 -0.04 -0.05 0.01 0.02 0.00
## personal.resilience_q5 0.12 0.75 -0.07 -0.14 0.01
## TC3 TC7 TC6 h2 u2
## balance.of.personal...others.interest_q5 -0.24 -0.24 -0.16 0.5611 0.44
## clear.governmental.communication_q5 -0.01 -0.01 -0.05 0.0071 0.99
## solidarity_q5 -0.10 0.89 -0.07 0.8118 0.19
## bipartisanship.and.international.cooperation_q5 -0.17 0.41 0.00 0.7282 0.27
## critical.thinking_q5 -0.01 -0.03 -0.07 0.6027 0.40
## embrace.new.tech_q5 0.71 -0.01 -0.09 0.6213 0.38
## agency...control_q5 -0.24 -0.25 -0.04 0.6637 0.34
## follow.rules_q5 -0.29 -0.11 0.01 0.5988 0.40
## optimism...positivity_q5 0.21 0.02 -0.05 0.7741 0.23
## learning.from.the.pandemics_q5 -0.03 0.09 -0.08 0.6105 0.39
## improve.communication_q5 -0.03 -0.01 -0.11 0.1634 0.84
## prosocial.behavior_q5 0.01 0.38 -0.04 0.7160 0.28
## long.term.orientation_q5 0.13 -0.22 -0.04 0.7227 0.28
## acknowledge.uncertainty.flexibility_q5 -0.02 0.11 0.07 0.5685 0.43
## living.in.the.moment_q5 0.81 -0.15 0.02 0.7007 0.30
## patience_q5 -0.10 -0.12 -0.02 0.6429 0.36
## perspective.taking_q5 0.06 0.30 -0.12 0.4448 0.56
## self.reflection.on.what.is.important_q5 -0.28 -0.29 -0.40 0.5339 0.47
## social.connectedness_q5 0.03 0.09 0.06 0.6476 0.35
## self.distancing_q5 -0.18 -0.11 0.24 0.4895 0.51
## strive.for.socio.economic.equality_q5 -0.11 0.00 0.72 0.5309 0.47
## sympathy...compassion_q5 0.01 -0.10 0.82 0.7499 0.25
## take.science.seriously_q5 -0.01 -0.01 -0.05 0.0071 0.99
## personal.resilience_q5 -0.08 -0.20 -0.06 0.6648 0.34
## com
## balance.of.personal...others.interest_q5 4.0
## clear.governmental.communication_q5 3.5
## solidarity_q5 1.1
## bipartisanship.and.international.cooperation_q5 2.1
## critical.thinking_q5 1.3
## embrace.new.tech_q5 1.4
## agency...control_q5 1.5
## follow.rules_q5 1.9
## optimism...positivity_q5 1.2
## learning.from.the.pandemics_q5 1.1
## improve.communication_q5 2.1
## prosocial.behavior_q5 2.0
## long.term.orientation_q5 1.5
## acknowledge.uncertainty.flexibility_q5 1.7
## living.in.the.moment_q5 1.1
## patience_q5 1.8
## perspective.taking_q5 3.9
## self.reflection.on.what.is.important_q5 5.8
## social.connectedness_q5 2.4
## self.distancing_q5 3.6
## strive.for.socio.economic.equality_q5 1.1
## sympathy...compassion_q5 1.2
## take.science.seriously_q5 3.5
## personal.resilience_q5 1.3
##
## TC2 TC1 TC8 TC5 TC4 TC3 TC7 TC6
## SS loadings 2.04 1.78 1.73 1.67 1.63 1.61 1.59 1.52
## Proportion Var 0.09 0.07 0.07 0.07 0.07 0.07 0.07 0.06
## Cumulative Var 0.09 0.16 0.23 0.30 0.37 0.44 0.50 0.57
## Proportion Explained 0.15 0.13 0.13 0.12 0.12 0.12 0.12 0.11
## Cumulative Proportion 0.15 0.28 0.41 0.53 0.65 0.77 0.89 1.00
##
## With component correlations of
## TC2 TC1 TC8 TC5 TC4 TC3 TC7 TC6
## TC2 1.00 0.05 0.06 -0.01 0.02 -0.03 0.02 -0.03
## TC1 0.05 1.00 0.08 -0.04 -0.06 -0.03 -0.02 -0.04
## TC8 0.06 0.08 1.00 -0.07 -0.01 0.06 -0.07 -0.06
## TC5 -0.01 -0.04 -0.07 1.00 -0.01 0.04 0.00 -0.02
## TC4 0.02 -0.06 -0.01 -0.01 1.00 -0.04 -0.01 -0.04
## TC3 -0.03 -0.03 0.06 0.04 -0.04 1.00 0.02 0.05
## TC7 0.02 -0.02 -0.07 0.00 -0.01 0.02 1.00 -0.01
## TC6 -0.03 -0.04 -0.06 -0.02 -0.04 0.05 -0.01 1.00
##
## Mean item complexity = 2.2
## Test of the hypothesis that 8 components are sufficient.
##
## The root mean square of the residuals (RMSR) is 0.07
## with the empirical chi square 160.06 with prob < 0.002
##
## Fit based upon off diagonal values = 0.66Similar to prior results, we can address the question of diversity by examining the degree to which scores across themes are reducible to common component, examining principal component analysis (PCA).
It appears that 8 components is as good a start to account for 25 themes for Question 5 (wisdom now), as any other. Once again, the scree plot line is pretty flat.
Again, keep in mind that reducing 24 items to 8 components is not parsimonious. Moreover, when we reduce the items to 8 components, the first component explains only 9 % of the variance, and in total 8 components explain 57% of variance.
Given that each theme by default explains 4.2% of the variance (1/23), this is not much.
Furthermore, each of these components is largely based on 1-2 items (loadings > .6), with exception of TC2 : bipartisanship & int.cooperation, learning from the pandemic, long-term orientation.
In short, principal components show substantial diversity, with the top factor social connectedness not even loading squarely on any of the components (and negatively loading on the component it has highest loading for - the one with positive loading for critical thinking).
8.6 Network Graph
Even after removing negligible correlations (r < .17), cluster analyses on top of the network graphs show five clusters, four of which are strongly inter-related (embrace new tech. and optimism/positivity, solidarity and prosocial behavior, self-distancing and pol. cooperation):
Five clusters:
* Solidarity & pol. cooperation
* Resilience-building: Establish agency, patience, prosociality,, optimism
* Focus on here & now (incl. role of new tech.)
* Meta-cognition: self-distancing, balancing different interests, critical thinking
* Social capital: social connectedness, improved communication, socio-econ equality, sympathy & compassion
Solidarity is in the center, connecting to other clusters.
8.7 Convergence vs. divergence of themes over time
Once again, we binned scores in the the same four groups as above: June, July, Sept/early Oct, and second part of Oct-early Dec.
Focus on meta-cognition is the top category across the summer and early fall, but becomes second frequent in late fall.
Focus on well-being enhancement and agency/control are more prevalent among researchers surveyed in late fall.
9 Total wisdom frequencies across questions
When examining categories over time, it becomes apparent that most frequent themes for wisdom either concern interpersonal prosociality (solidarity, social connectedness) or societal level-prosocility (asking for structural changes for a fair and just society), as well as meta-cognitive strategies (perspective-taking, sympathy & compassion, long-term focus, acknowledge unceryainty, self-distancing, critical thinking)
10 Relationship of Forecasts & Given Advice
A key question concerns type of advice scientists recommend for different (positive and negative forecasts) for positive and negative changes after the pandemic.
To address this question, we can examine dependencies between themes mentioned for a particular type of outcome, and subsequent mentioning of themes needed for this outcome.
10.1 Positive Consequences
Given that some themes are mentioned both as outcomes of the pandemic (i.e., forecasts) and the advice/wisdom needed to sustain positive change (e.g., critical thinking, live in the moment, political/structural change), it would be trivial to see relevant relationships. However, it would be more interesting to see relationships across themes from forecast (Q1) and corresponding advice (Q2).
10.1.1 Heatmap
One way to address this question is to examine the heatmap, with forecast themes on the vertical axis and the advice themes on the horizontal axis.
As one can see below, some of the results are indeed quite trivial. Beyond same themes mentioned for forecasts and advice about wisdom needed, another trivial observation is that greater care for elderly depends on willingness to learn from the pandemic. Nevertheless, some further observations are noteworthy. I focus on r > .3 as a cut off:
- greater science interest in the future depends on critical thinking;
- greater social connectedness and optimism/positivity depend on improved communication;
- greater health & wellbeing depends on improved work-life balance , focus on living in the moment & ability to compromise/balance diverse interests;
- greater resilience depends on sympathy & compassion;
- greater resilience depends on sympathy & compassion;
- learning from the pandemic in the future depends on political cooperation;
- greater solidarity depends on awareness of shared humanity;
- greater transition to new technologies and reconsideration of habits depend on personal resilience;
- strongest relationship: greater resilience, embacing new tech, and reconsideration of habits depend on acknowledgement of uncertainty.
10.1.2 Q1-Q2 Edge Binding
A different, and perhaps more intuitive way to examine the relationship between themes mentioned for forecasts and advice is to examine connections between “edges” formed in the network of QUestions concerning forecasts and the advice. Here, full information from the network is used to optimally visualize connections between different themes in the network.
Below, you see an edge-binding diagram visualizing the relationships. On the right, you see themes for forecasts (Q1) and on the left you see type of advice recommended (Q2). When you navigate the cursor on each theme, the graph will show you what other themes it is mostly connected to, both within the same question and across questions. As for the other network visualizations, I used r < .17 as a cut-off here.
For instance, if you navigate to Q1_Gratitude, you will see that it is related not only to Q1_living in a moment and Q1_new tech, but also Q2 focus on what’s important, Q2 living in the moment, Q2 critical thinking, and Q2 balance of diverse interests.
By inspecting all Q1 themes, we can see that themes followed by greatest number of advice-themes are:
Gratitude;
Greater appreciation of Nature;
Resilience;
Solidarity.
Conversely, by inspecting all Q2 themes, we can see that themes with greatest number of implications for forecasts are:
Acknowledgment of uncertainty;
Solidarity;
Self-distancing.
10.2 Negative Consequences
We can do the same analysis for Questions 3 (negative forecasts) and 4 (wisdom needed to prevent these forecasts).
10.2.1 Heatmap
Again, I will start with a heatmap, with forecast themes on the vertical axis and the advice themes on the horizontal axis.
Given the negative nature of the forecasts, we would not expect as many trivial results (except for things like: who do you need to reduce socio-econ or edu equality? Fight against socio-econ inequality! Or: "how do we prevent dispair? Be optimistic/positive :)). Once again, I focus on r > .3 as a cut off:
- combating estrangement/alienation, loneliness, decline in wellbeing was linked to the notion of social connectedness;
- combating economic hardships and rise in despair was linked to social support;
- combating rising social inequality was linked to solidarity;
- strongest relationships: combating problems in intimate relations, educational inequality, and child development issues were linked to ability to balance/reach a compromise across diverse interests;
- combating decline in autobiographic memory, estrangement/alienation, and well-being were linked to contextualizing one’s life experiences;
- combating irrationality depends on living in a moment/mindfulness,gratitude, and self-distancing;
- combating low trust in science was linked to self-distancing;
- combating political conflict was linked to appreciation of the concept of shared humanity;
10.2.2 Q3-Q4 Edge Binding
Once again, below you see an edge-binding diagram - a different way to visualize the relationships. On the right, you see themes for negative forecasts (Q3) and on the left you see type of advice recommended (Q4). When you navigate the cursor on each theme, the graph will show you what other themes it is mostly connected to, both within the same question and across questions. As for the other network visualizations, I used r < .17 as a cut-off here.
For instance, if you navigate to Q1_Gratitude, you will see that it is related not only to Q1_living in a moment and Q1_new tech, but also Q2 focus on what’s important, Q2 living in the moment, Q2 critical thinking, and Q2 balance of diverse interests.
By inspecting all Q3 themes, we can see that themes followed by greatest number of advice-themes are:
Estrangement/alienation;
Despair;
Self-centeredness;
Irrationality;
Loneliness;
Pessimism;
Conversely, by inspecting all Q4 themes, we can see that themes with greatest number of implications for forecasts are:
Self-distancing;
Political/structural change;
Perspective-taking;
Patience;
Gratitude.